Machine Learning in Bioinformatics

Máster en Bioinformática Aplicada a Medicina Personalizada y Salud (Curso 2025-2026)

Scheduling

First week (09-10 April 2026)

• Day 1 (09.04.2026):
  • 2/3 Theory
  • 1/3 Project: Session I (project presentation)
• Day 2 (10.04.2026):
  • 1/2 Theory
  • 1/2 Project: Session II

Second week (20-21 April 2026)

• Day 3 (20.04.2026):
  • Practice: Hands-On (Machine Learning Basics in Python with scikit-learn Part I)
• Day 4 (21.02.2025):
  • 2/3 Practice: Hands-On (Machine Learning Basics in Python with scikit-learn Part II)
  • 1/3 Practical tips for Machine Learning

Third week (27-30 April 2025)

• Day 5 (27.04.2026):
  • 2/3 Theory
  • 1/3 Project: Session III (with problem and dataset presentation HITO-1)
• Day 6 (28.03.2026):
  • 1/2 Theory: presentation of a real case-study (the PolyDeep project)
  • 1/2 Project: Session IV
• Day 7 (29.03.2026):
  • Project: Session V
• Day 8 (30.03.2026):
  • Project Session: VI (with results presentation HITO-2)

Practice

Install Conda in your system

Download and install Miniconda from: https://www.anaconda.com/download/success

Creating the Conda environment

Run the following command to create the Conda environment for the hands-on practice sessions:

conda env create -f environment.yml

And then activate it by running:

conda activate machine-learning

Download the breast cancer dataset

During the hands-on sessions (Machine Learning Basics in Python with scikit-learn), we are going to use the Breast Cancer Data available here.

This file came from the UCI Machine Learning Repository. More information about this dataset can be found here and here.

To download it again, run the following commands:

mkdir data

wget https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/wdbc.data -O data/wdbc.data

sed -i '1iid,diagnosis,radius_mean,texture_mean,perimeter_mean,area_mean,smoothness_mean,compactness_mean,concavity_mean,concave points_mean,symmetry_mean,fractal_dimension_mean,radius_se,texture_se,perimeter_se,area_se,smoothness_se,compactness_se,concavity_se,concave_points_se,symmetry_se,fractal_dimension_se,radius_worst,texture_worst,perimeter_worst,area_worst,smoothness_worst,compactness_worst,concavity_worst,concave points_worst,symmetry_worst,fractal_dimension_worst' data/wdbc.data

Jupyter Notebook

The Jupyter notebook for the hands-on sessions is available here.

Project

The information about the project is available here.

References

• Practical Statistics for Data Scientists: 50 Essential Concepts
• Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition

Additional Resources

Papers

• Ten quick tips for machine learning in computational biology [10.1186/s13040-017-0155-3]
• DOME: recommendations for supervised machine learning validation in biology [10.1038/s41592-021-01205-4]
• The ABC recommendations for validation of supervised machine learning results in biomedical sciences [10.3389/fdata.2022.979465]

Libraries

• scikit-learn: machine learning in Python
• Imbalanced-learn
• XGBoost
• LIBSVM -- A Library for Support Vector Machines

Lectures

• Stratified Splitting with train_test_split Using Target and Group Variables
• Combining sklearn’ GridSearchCV with Leave-One-Out Cross-Validation (LOOCV)